Post on 11-Mar-2020
Modern Power System Dynamic Performance Improvement through Big Data Analysis
Yajun WangDepartment of Electrical Engineering and Computer Science
Committee Members:Dr. Héctor A. Pulgar, Dr. Yilu Liu, Dr. Hairong Qi, Dr. Russell Zaretzki
Ph.D. Dissertation DefenseApril 4, 2019
Outline
• Introduction
• Inertia Distribution Estimation in Power Systems
• Actuator Placement for Enhanced System Dynamics
• Real-time Security Assessment and Cascading Failure Analysis
• Conclusion and Future Work
2
Outline
• Introduction
• Inertia Distribution Estimation in Power Systems
• Actuator Placement for Enhanced System Dynamics
• Real-time Security Assessment and Cascading Failure Analysis
• Conclusion and Future Work
3
Background and Motivation
Modern power system transformation
Voltage StabilityTransient Stability
4 8 12 16 200
0.6
0.7
0.8
0.9
1
1.1
1.2
Sec
Bus Voltage (pu)
4 8 12 16 200
0
200
400
600
800
1000
Sec
Generator Rotor Angle (Degrees)
Frequency concerns
4
New challenges
Traditional model-based methods are finding difficulties to adapt to the changes
Background and Motivation
Massive PMU and FDR deployed in the system
5
Huge development in AI techniques
Big data analysis extract valuable information from PMU data to enhance system dynamics
Main Contributions
6
• A pioneer to find the physical location of COI and create accurateinertia distribution map
Power system
data
Inertia distribution estimation
Power system
dynamics
Y. Wang, H. Silva-Saravia, H. Pulgar-Painemal, Estimating inertia distribution to enhance powersystem dynamics, North American Power Symposium, Morgantown, WV, USA, Sep 2017H. Pulgar-Painemal, Y. Wang, H. Silva-Saravia, On inertia distribution, inter-area oscillations andlocation of electronically-interfaced resources, IEEE Transactions on Power Systems, Vol. 33, No.1, 2018, pp. 995-1003Y. Wang, H. Silva-Saravia, H. Pulgar-Painemal, Equivalent inertia constant expression for powersystems, IEEE Transactions on Power Systems, 2019 (In preparation)
Main Contributions
7
• A pioneer to find the physical location of COI and create accurateinertia distribution map
Power system
data
Inertia distribution estimation
Actuator placement
Power system
dynamics
• Placed the actuators at the bus that are far away from the COI bus,increasing damping ratio to 14% and reducing the computationalcomplexity
• Created general and effective guidance for planners, considering moresystem features and more dynamic problems
H. Pulgar-Painemal, Y. Wang, H. Silva-Saravia, On inertia distribution, inter-area oscillations andlocation of electronically-interfaced resources, IEEE Transactions on Power Systems, Vol. 33, No.1, 2018, pp. 995-1003Y. Wang, H. Silva-Saravia, H. Pulgar-Painemal, Actuator placement for enhanced grid dynamicperformance: A machine learning approach, IEEE Transactions on Power Systems (Early access),2019H. Silva-Saravia, Y. Wang, H. Pulgar-Painemal, Determining wide-area signals and locations ofregulating devices to damp inter-area oscillations through eigenvalue sensitivity analysis usingDIgSILENT Programming Language, Advanced Smart Grid Functionalities based on PowerFactory, Springer, 2018, pp. 153-179H. Silva-Saravia, Y. Wang, H. Pulgar-Painemal, K. Tomsovic, Oscillation energy based sensitivityanalysis and control for multi-mode oscillation systems, IEEE PES General Meeting, Portland, OR,USA, August 2018
Main Contributions
8
• A pioneer to find the physical location of COI and create accurateinertia distribution map
Power system
data
Inertia distribution estimation
Actuator placement
Power system
dynamics
• Placed the actuators at the bus that are far away from the COI bus,increasing damping ratio to 14% and reducing the computationalcomplexity
• Created general and effective guidance for planners, considering moresystem features and more dynamic problems
Operational studies
• Achieved the highest accuracy and lower computational time for real-time security analysis
• Identified the most critical links and generate probability tree modelfor cascading failure analysis
Y. Wang, H. Pulgar-Painemal, K. Sun, Online analysis of voltage security in a microgrid usingconvolutional neural networks, IEEE PES General Meeting, Chicago, IL, USA, July 2017Y. Wang, W. Ju, H. Pulgar-Painemal, Cascading failure key link identification and tree modelgeneration: A data driven approach, IEEE PES General Meeting, 2020 (In preparation)
Main Contributions
9
• A pioneer to find the physical location of COI and create accurateinertia distribution map
Power system
data
Inertia distribution estimation
Actuator placement
Power system
dynamics
• Placed the actuators at the bus that are far away from the COI bus,increasing damping ratio to 14% and reducing the computationalcomplexity
• Created general and effective guidance for planners, considering moresystem features and more dynamic problems
Operational studies
• Achieved the highest accuracy and lower computational time for real-time security analysis
• Identified the most critical links and generate probability tree modelfor cascading failure analysis
Power system dynamics is improved through big data analysis
Outline
• Introduction
• Inertia Distribution Estimation in Power Systems
• Actuator Placement for Enhanced System Dynamics
• Real-time Security Assessment and Cascading Failure Analysis
• Conclusion and Future Work
10
Research Gap and Highlighted Contribution
11
Inertia Definition• Provided by spinning mass of directly synchronized
electrical machines• Defines reaction of frequency to a sudden power
imbalance
Research Gap and Highlighted Contribution
12
Inertia Definition• Provided by spinning mass of directly synchronized
electrical machines• Defines reaction of frequency to a sudden power
imbalance
Low inertia
High inertia
System with higher inertia has more ability to resist change
Research Gap and Highlighted Contribution
13
Current Research Gap
G1G2
G3Inertia constant
Accelerating power
Machine speed
• Center of Inertia (COI) reference defines aweighted function of all machine rotorangle and speed
No physical location of COINo inertia distribution estimation
Research Gap and Highlighted Contribution
14
G1
Heavier inertia
Lighter inertia
Research Gap and Highlighted Contribution
15
G2
Heavier inertia
Lighter inertia
Research Gap and Highlighted Contribution
16
G3Heavier inertia
Lighter inertia
Research Gap and Highlighted Contribution
17
G1G2
G3Find physical location of COI
Explore inertia distribution in the system for every location
Show potential application of inertia distribution results
Main Contribution
H. Pulgar-Painemal, Y. Wang, H. Silva-Saravia,On inertia distribution, inter-area oscillationsand location of electronically-interfacedresources, IEEE Transactions on PowerSystems, Vol. 33, No. 1, 2018, pp. 995-1003Y. Wang, H. Silva-Saravia, H. Pulgar-Painemal,Equivalent inertia constant expression for powersystems, IEEE Transactions on Power Systems,2019 (In preparation)
Y. Wang, H. Silva-Saravia, H. Pulgar-Painemal, Estimating inertia distribution toenhance power system dynamics, NorthAmerican Power Symposium, Morgantown,WV, USA, Sep 2017
Find Physical Location of COI
18
Mathematical Proof
Equivalent swing equation
Equivalent inertia constant
SG Acceleration power
Find Physical Location of COI
19
Mathematical Proof
Equivalent swing equation
Equivalent inertia constant
SG Acceleration power
Find Physical Location of COI
20
Mathematical Proof Equivalent swing equation
Equivalent inertia constant
SG Acceleration power
COI physical location
At COI bus
Expression for COI
H. Pulgar-Painemal, Y. Wang, H. Silva-Saravia, On inertia distribution, inter-area oscillations andlocation of electronically-interfaced resources, IEEE Transactions on Power Systems, Vol. 33, No. 1,2018, pp. 995-1003
Find Physical Location of COI
21
Mathematical Proof Equivalent swing equation
Equivalent inertia constant
SG Acceleration power
COI physical location
At COI bus
Expression for COI
H1=5s H2=1s-25s
Find Physical Location of COI
22
Mathematical Proof
H1=5s H2=1s
COICOI physical location
At COI bus
Expression for COI
=0.167
Find Physical Location of COI
23
Mathematical Proof
H1=5s H2=2s
COICOI physical location
At COI bus
Expression for COI
=0.286
Find Physical Location of COI
24
Mathematical Proof
H1=5s H2=5s
COICOI physical location
At COI bus
Expression for COI
=0.5
Find Physical Location of COI
25
Mathematical Proof
H1=5s H2=25s
COI
COI physical location
At COI bus
Expression for COI
=0.833
Y. Wang, H. Silva-Saravia, H. Pulgar-Painemal, Equivalent inertia constantexpression for power systems, IEEE Transactions on Power Systems, 2019 (Inpreparation)
Find Physical Location of COI
26
Mathematical Proof
H1=5s H2=5s
COI
COI is the bus that its changes in angle and frequency are minimal
=0.5At COI
Measurement-based Inertia Distribution Estimation
27
Center of Frequency Index
• Reports how far away a particular bus isto the COI bus
COI
Furthest to COI
Closest to COI
H1=H3=6s, H3=H4=4.25s, length of
the lines between buses X1 and buses X3 is five times the value
in the base case
Measurement-based Inertia Distribution Estimation
28
Case 1: A meshed system
COI is near the machine with a larger inertia constant and larger impedance parameters
H1=H2=H3=H4=4.25s
Closest to COI
H3=8.5s, H1=H2=H4=4.25s
Measurement-based Inertia Distribution Estimation
29
Case 2: IEEE 39-bus test system
Bus 1 is closest to the COI bus, Bus 20 is the furthest point from the COI bus
Closest to COI
Furthest to COI
Measurement-based Inertia Distribution Estimation
30
Case 3: Real systems: Chilean power system and a real system
Closest to COI
Inertia distribution map is created for real system condition
Applications of Inertia Distribution
31
• Actuator placement
• Optimal placement of PMUs
• Power system dynamic performances can be improved by understanding of the system inertia distribution
PMU
PMUPMU
PMU
Y. Wang, H. Silva-Saravia, H. Pulgar-Painemal, Estimatinginertia distribution to enhance power system dynamics, NorthAmerican Power Symposium, Morgantown, WV, USA, Sep 2017
Summary
• Obtained physical location of COI from theoretical proof and validated them indynamic simulation
• Created system inertia distribution map through a measurement based estimationapproach
• The understanding of the system inertia distribution can improve power systemdynamic performances in problems such as optimal PMU and control actuatorplacement
32
Outline
• Introduction
• Inertia Distribution Estimation in Power Systems
• Actuator Placement for Enhanced System Dynamics
• Real-time Security Assessment and Cascading Failure Analysis
• Conclusion and Future Work
33
Research Gap and Highlighted Contribution
34
Traditional ESS placement methods
Focused on static state or economic cost
Formed asoptimization
problem
• Control component to damp oscillations• Mainly for Energy storage system, can also be enabled with wind turbine or other converter/inverter based
machines
Control actuator
Fail to fully capture some important aspects of the system dynamics
Solved byheuristic, stochastic
optimization approaches
Current Research Gap
Research Gap and Highlighted Contribution
35
Main ContributionUse inertia distribution to allocate control actuators
Extract most important features to create guidelines to allocate control actuators System
featuresH. Pulgar-Painemal, Y. Wang, H. Silva-Saravia, On inertiadistribution, inter-area oscillations and location ofelectronically-interfaced resources, IEEE Transactions onPower Systems, Vol. 33, No. 1, 2018, pp. 995-1003
H. Pulgar-Painemal, Y. Wang, H. Silva-Saravia, On inertiadistribution, inter-area oscillations and location ofelectronically-interfaced resources, IEEE Transactions onPower Systems, Vol. 33, No. 1, 2018, pp. 995-1003H. Silva-Saravia, Y. Wang, H. Pulgar-Painemal, Determiningwide-area signals and locations of regulating devices to dampinter-area oscillations through eigenvalue sensitivity analysisusing DIgSILENT Programming Language, Advanced SmartGrid Functionalities based on Power Factory, Springer,2018, pp. 153-179
Actuator Placement using Inertia Distribution Index
36
Residue when in ESS is connected
At COI bus
• The residue calculated from the transfer function for the dominant mode is quadratic to thedistance from a bus to the COI bus
• Less residue indicates less controllability and observability
Mathematical Proof
Actuator should be placed at the higher residue locations
Actuator Placement using Inertia Distribution Index
37
Residue when in ESS is connected
Simulation Validation
Inertia distribution index
Actuator should be placed at the bus that is further to COI
Actuator Placement using Inertia Distribution Index
38
Case 1: IEEE 39-bus test system
Location No actuator Closest to COI bus
Furthest to COI bus
Damping ratio 5% 8.04% 19.3%
Actuator should be placed at the bus that is further to the COI bus
Actuator Placement using Inertia Distribution Index
39
Case 2: Chilean power system
Location No actuator Closest to COI bus
Furthest to COI bus
Damping ratio 0.53% 6.0% 13.1%
H. Pulgar-Painemal, Y. Wang, H. Silva-Saravia, On inertia distribution, inter-area oscillations and location ofelectronically-interfaced resources, IEEE Transactions on Power Systems, Vol. 33, No. 1, 2018, pp. 995-1003H. Silva-Saravia, Y. Wang, H. Pulgar-Painemal, Determining wide-area signals and locations of regulatingdevices to damp inter-area oscillations through eigenvalue sensitivity analysis using DIgSILENT ProgrammingLanguage, Advanced Smart Grid Functionalities based on Power Factory, Springer, 2018, pp. 153-179
Summary
• Verified the physical location of COI through analytical proof• Find a simple rule to place the actuators, which can effectively increase the system
dynamics and hugely reduce the calculation complexity
40
Actuator Placement using A machine Learning Approach
41
More system features
More system dynamics
Proposed Idea
Actuator Placement using A machine Learning Approach
42
• Extracts the significant features of a potential location/bus that interpret system dynamic behavior• Obtain general guidelines from the model for placing control actuators to improve system
dynamic performance
Flowchart
Actuator Placement using A machine Learning Approach
43
Overall performance indexInput Output
Stage 2: Data set construction
Topological data
Physical data
Operational data
System integrated data
Data source Feature layers
Actuator Placement using A machine Learning Approach
44
Stage 2: Data set construction (IEEE 39-bus system visualization)
Topological feature Inertia distribution feature
Voltage magnitude feature
Input: System features
Actuator Placement using A machine Learning Approach
45
Stage 2: Data set construction (IEEE 39-bus system visualization)Output: System dynamic response
• The use of all three dynamic performanceindices is needed to asses the overallsystem performance
• Labels K ={1,2,3,4} are marked for all thebuses with k-means clustering algorithm
Voltage
Tran
sien
t
Actuator Placement using A machine Learning Approach
46
Stage 3: Feature Extraction and Analytical Model Generation
Actuator Placement using A machine Learning Approach
47
Stage 3: Feature Extraction and Analytical Model Generation
LASSO (Least Absolute Shrinkage and Selection Operator )
Output Input
the parameter that controls the strength of the penalty
• Use linear regression model to find the coefficients of features• When increases, coefficients of less important features will
be shrank to 0• Extract most important features by the shrinkage
Actuator Placement using A machine Learning Approach
48
Stage 3: Feature Extraction and Analytical Model Generation
• The most influential features are inertia distribution, topologydistribution and voltage angle
• The operational features are not significant with higher RE level
Prediction model
Evaluation indices
Case 1: 39-bus system
Actuator Placement using A machine Learning Approach
49
Stage 3: Feature Extraction and Analytical Model GenerationCase 2: 118-bus system
• The most influential features are voltage angle, faultlocation and impedance distribution
• Three dynamic indices are needed for overall systemdynamics
• Cluster number K will not affect the overall accuracy
Overall performance index
Frequency Voltage
Y. Wang, H. Silva-Saravia, H. Pulgar-Painemal, Actuator placement for enhanced griddynamic performance: A machine learning approach, IEEE Transactions on PowerSystems (Early access) , 2019
Actuator Placement using A machine Learning Approach
50
Stage 4: Guidelines for System Planners
• System physical features play a more important role than topological and operationalfeatures
• Among all system topological and physical features, inertia distribution index, topologydistribution index and impedance distribution index are the most critical ones
• Among all operational features:1) Bus voltage angle is the most significant one2) Fault location affects the performance of the buses when RE penetration level is low.
When RE penetration level is above 20%, the impact of fault location or duration time is lessrelevant
3) The RE penetration levels and loading levels do not affect the bus performance as muchas active and reactive power output
Summary
• Determine the most significant features in the placement of control actuatorsconsidering dynamic performance
• Provide general criteria and guidance for system planners to place the controlresources in a given system for improving oscillation damping, voltage andtransient stability
51
Outline
• Introduction
• Inertia Distribution Estimation in Power Systems
• Actuator Placement for Enhanced System Dynamics
• Real-time Security Assessment and Cascading Failure Analysis
• Conclusion and Future Work
52
Real-time Voltage Security Assessment Voltage security assessment in a micro-grid
Flow chart of voltage security analysis
Load switching Line outage Three phase short circuit
Stable examples
Unstable examples
53
Real time data samples
Real-time Voltage Security Assessment Case Study in IEEE 14-bus system
Structure of proposed CNN
Performance of Back-Propagation Neural Networks, Decision Tree, Support Vector
Machine, and Convolutional Neural Networks
• CNN has the best performance due the 2-D data transformationand hidden layer construction
• Deep learning algorithm has great potential in the modern powersystem studies
54
Y. Wang, H. Pulgar-Painemal, K. Sun, Online analysis of voltage security in a microgrid usingconvolutional neural networks, IEEE PES General Meeting, Chicago, IL, USA, July 2017
Critical Link Identification in Cascading Failure Analysis
55
Key Link Identification in WECC system
Distance matrix
Critical Link Identification in Cascading Failure Analysis
56
Cascading Failure Tree Model
• Key links are obtained using 400,000 data samples
• Cascading failure tree model based on probability calculation has been generated
Y. Wang, W. Ju, H. Pulgar-Painemal, Cascading failure key link identification and tree modelgeneration: A data driven approach, IEEE PES General Meeting, 2020 (In preparation)
Summary
• Achieve higher voltage security assessment accuracy using a CNN-based method
• Understand the cascading procedure better with statistic based model
57
Outline
• Introduction
• Inertia Distribution Estimation in Power Systems
• Actuator Placement for Enhanced System Dynamics
• Real-time Security Assessment and Cascading Failure Analysis
• Conclusion and Future Work
58
Conclusion
• The understanding of the system inertia distribution can improve power system dynamic performance
• Allocation of actuators can enhance system dynamic response. Furthermore, For system with single-dominant oscillation mode:
1) Inertia distribution index can help to allocate control actuators in the; the actuators shouldbe placed at the bus further to the COI bus
For system with multi oscillation mode and multi stability problems:1) System physical features play a more important role than topological and operational
features2) Among all system topological and physical features, inertia distribution index, topology
distribution index and impedance distribution index are the most critical ones
• Higher security assessment accuracy can be achieved using a deep learning method and critical linkscan be identified using a tree model
59
Future Work
60
• Study active power control enabled with inverter-based machine and evaluate the emulated inertiaresponse
• Design model-free and data-driven damping controller
PublicationY. Wang, H. Silva-Saravia, H. Pulgar-Painemal, Actuator placement for enhanced grid dynamic performance: Amachine learning approach, IEEE Transactions on Power Systems, (Early access), 2019H. Pulgar-Painemal, Y. Wang, H. Silva-Saravia, On inertia distribution, inter-area oscillations and location ofelectronically-interfaced resources, IEEE Transactions on Power Systems, Vol. 33, No. 1, 2018, pp. 995-1003Y. Wang, H. Silva-Saravia, H. Pulgar-Painemal, Equivalent inertia constant expression for power systems, IEEETransactions on Power Systems, 2019 (In preparation)Y. Wang, H. Silva-Saravia, H. Pulgar-Painemal, Estimating inertia distribution to enhance power system dynamics,North American Power Symposium, Morgantown, WV, USA, Sep 2017Y. Wang, H. Pulgar-Painemal, K. Sun, Online analysis of voltage security in a microgrid using convolutional neuralnetworks, IEEE PES General Meeting, Chicago, IL, USA, July 2017H. Silva-Saravia, Y. Wang, H. Pulgar-Painemal, K. Tomsovic, Oscillation energy based sensitivity analysis andcontrol for multi-mode oscillation systems, IEEE PES General Meeting, Portland, OR, USA, August 2018Y. Wang, W. Ju, H. Pulgar-Painemal, Cascading failure key link identification and tree model generation: A datadriven approach, IEEE PES General Meeting, 2020 (In preparation)H. Silva-Saravia, Y. Wang, H. Pulgar-Painemal, Determining wide-area signals and locations of regulating devicesto damp inter-area oscillations through eigenvalue sensitivity analysis using DIgSILENT Programming Language,Advanced Smart Grid Functionalities based on Power Factory, Springer, 2018, pp. 153-179
61
Acknowledgements
This work was supported primarily by the National Science Foundation under Grant No. 1509114, the Engineering Research
Center Program of the National Science Foundation and the Department of Energy under NSF Award No. EEC-1041877 and the
CURENT Industry Partnership Program.
Other US government and industrial sponsors of CURENT research are also gratefully acknowledged.
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